Prosody labeling is an important part of many speech synthesis and speech understanding processes and systems. Among all prosody events, accent is often of particular importance. Manual accent labeling, for its own sake or to support an automatic labeling technique, is often expensive, time consuming, and can be error prone given inconsistency between labelers. As a result, auto-labeling is often a more desirable alternative.
Currently, there are some known methods that, to some extent, support accent auto-labeling. However, it is common that all or a portion of the classifiers used for labeling accented/unaccented syllables are trained from manually labeled data. Due to circumstances such as the cost of labeling, the size of manually labeled data is often not large enough to train classifiers with a high degree of precision. Moreover, it is not necessarily easy to find individuals qualified to the labeling in an efficient and effective manner.
The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter.